code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : Any = '''▁... | 711 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
a : str = True
except (ImportError, ModuleNotFoundError):
a : List[str] = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def _UpperCamelCase ( ... | 19 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class a_ :
'''simple docstring'''
a : torch.Tensor # [batch_size x 3]
a : torch.Tensor # [batch_size x 3]
a : torch.Tensor # [batc... | 712 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_... | 19 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[Any] = logging.get_logger(__name__)
a : List[Any] = {
'''BridgeTower/bridgetower-base''': '''https://huggi... | 713 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ... | 19 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
a : Any ... | 714 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from dat... | 19 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : List[str] = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFI... | 715 |
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorForma... | 19 | 0 |
"""simple docstring"""
import re
from ..utils import cached_file
# docstyle-ignore
a : Dict = '''
Human: <<task>>
Assistant: '''
a : List[str] = '''huggingface-tools/default-prompts'''
a : Union[str, Any] = {'''chat''': '''chat_prompt_template.txt''', '''run''': '''run_prompt_... | 716 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Tuple = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not is_torch_available():
r... | 19 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils... | 717 |
"""simple docstring"""
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
a : int = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def _UpperCamelCase ( _A ) ... | 19 | 0 |
import numpy as np
class a_ :
def __init__( self : List[str] , __UpperCamelCase : Dict=None , __UpperCamelCase : str=None , __UpperCamelCase : str=None , __UpperCamelCase : List[str]=None , __UpperCamelCase : ... | 718 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patc... | 19 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Tuple = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not is_torch_available():
r... | 719 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class a_ ( _UpperC... | 19 | 0 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class a_ ( _UpperCAmelCase ):
a : Union[str, Any] = (DPMSolverS... | 720 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
a : Optional[Any] = '''\
@misc{chen2021evaluating,
title={Eva... | 19 | 0 |
from functools import lru_cache
@lru_cache
def _UpperCamelCase ( _A ) -> int:
"""simple docstring"""
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import do... | 721 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _UpperCamelCase ( _A , _A , _A , _A , _A ) -> np.array:
"""simple docstring"""
_UpperCAmelCase = int(np.ceil((x_end - xa) / step_size ) )
_U... | 19 | 0 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transform... | 700 |
"""simple docstring"""
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditional... | 19 | 0 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_com... | 701 |
"""simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def _UpperCamelCase ( _A , _A=None ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = N... | 19 | 0 |
"""simple docstring"""
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
a ... | 702 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class a_ ( _UpperCAmelCase ):
a : Any = ['image_processor', 'tokenizer']
a : Optional[int] = 'AutoImageProcessor'
a : An... | 19 | 0 |
"""simple docstring"""
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _UpperCamelCase ( _... | 703 |
"""simple docstring"""
import colorsys
from PIL import Image # type: ignore
def _UpperCamelCase ( _A , _A , _A ) -> float:
"""simple docstring"""
_UpperCAmelCase = x
_UpperCAmelCase = y
for step in range(_A ): # noqa: B007
... | 19 | 0 |
"""simple docstring"""
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
a : List[Any] = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
... | 704 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a_ ( nn.Module ):
def __init__( self : List[str] , __UpperCamelCase : int = 16 , __UpperCamelCase : ... | 19 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class a_ :
def __init__( self : List[str] ) ->Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = {}
def ... | 705 |
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCamelCase ( _A , _A , _A ) -> List[Any]:
"""simple ... | 19 | 0 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a_ ( nn.Module ):
def __init__( self : List[str] , __UpperCamelCase : int = 16 , __UpperCamelCase : int = 88 , __Uppe... | 706 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
a : str = '''examples/'''
a : List[str] = {
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(r'''^__... | 19 | 0 |
"""simple docstring"""
import math
def _UpperCamelCase ( _A ) -> str:
"""simple docstring"""
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while num > 0:
_UpperCAmelCase = num % 8
_UpperCAmelCase = octal + (remainder * math... | 707 |
"""simple docstring"""
from __future__ import annotations
def _UpperCamelCase ( _A ) -> None:
"""simple docstring"""
create_state_space_tree(_A , [] , 0 , [0 for i in range(len(_A ) )] )
def _UpperCamelCase ( _A , _A , ... | 19 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision... | 708 |
"""simple docstring"""
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test... | 19 | 0 |
"""simple docstring"""
def _UpperCamelCase ( _A , _A ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = """"""
for i in table:
res += inp[i - 1]
return res
def _UpperCamelCase ( _A ) -> Optional[Any]:
"""simple docs... | 709 |
"""simple docstring"""
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_t... | 19 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
... | 710 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_de... | 19 | 0 |
"""simple docstring"""
from math import pi, sqrt
def _UpperCamelCase ( _A ) -> float:
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(_A ) not in (0, 0.5):
raise N... | 711 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
a : str = True
except (ImportError, ModuleNotFoundError):
a : List[str] = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def _UpperCamelCase ( ... | 19 | 0 |
"""simple docstring"""
import sys
from pathlib import Path
a : Optional[int] = Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from... | 712 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_... | 19 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_table,... | 713 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ... | 19 | 0 |
"""simple docstring"""
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data... | 714 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from dat... | 19 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
a : Any = False
class a_ ( unittest.TestCase ):
pass
... | 715 |
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorForma... | 19 | 0 |
"""simple docstring"""
from timeit import timeit
a : Any = {
'''MALAYALAM''': True,
'''String''': False,
'''rotor''': True,
'''level''': True,
'''A''': True,
'''BB''': True,
'''ABC''': False,
'''amanaplanacanalpanama''': True, # "a man a plan a canal panama"
}
# Ensure o... | 716 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Tuple = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not is_torch_available():
r... | 19 | 0 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
a : List[Any] = '''\
'''
a : List[Any] = '''
Perplexity (PPL) is one of the most common me... | 717 |
"""simple docstring"""
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
a : int = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def _UpperCamelCase ( _A ) ... | 19 | 0 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
a : List[str] = datasets.load_iris()
a : int = np.array(data['''data'''])
a : Optional[int] = np.array(data['''target'''])
a : Optional[int] ... | 718 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patc... | 19 | 0 |
"""simple docstring"""
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_t... | 719 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class a_ ( _UpperC... | 19 | 0 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def _UpperCamelCase ( _A ) -> List[str]:
"""si... | 720 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
a : Optional[Any] = '''\
@misc{chen2021evaluating,
title={Eva... | 19 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ... | 721 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _UpperCamelCase ( _A , _A , _A , _A , _A ) -> np.array:
"""simple docstring"""
_UpperCAmelCase = int(np.ceil((x_end - xa) / step_size ) )
_U... | 19 | 0 |
"""simple docstring"""
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTe... | 700 |
"""simple docstring"""
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditional... | 19 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, r... | 701 |
"""simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def _UpperCamelCase ( _A , _A=None ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = N... | 19 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
a : str = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
''' Dist... | 702 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class a_ ( _UpperCAmelCase ):
a : Any = ['image_processor', 'tokenizer']
a : Optional[int] = 'AutoImageProcessor'
a : An... | 19 | 0 |
"""simple docstring"""
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHEC... | 703 |
"""simple docstring"""
import colorsys
from PIL import Image # type: ignore
def _UpperCamelCase ( _A , _A , _A ) -> float:
"""simple docstring"""
_UpperCAmelCase = x
_UpperCAmelCase = y
for step in range(_A ): # noqa: B007
... | 19 | 0 |
"""simple docstring"""
from __future__ import annotations
def _UpperCamelCase ( _A , _A , _A ) -> tuple[float, list[float]]:
"""simple docstring"""
_UpperCAmelCase = list(range(len(_A ) ) )
_UpperCAmelCase = [v / w for v, w in... | 704 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a_ ( nn.Module ):
def __init__( self : List[str] , __UpperCamelCase : int = 16 , __UpperCamelCase : ... | 19 | 0 |
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
a : List[Any] = HUGGINGFACE_HUB_CACHE
a : Optional[int] = '''config.json'''
a : List[Any] = '''diffusion_pytorch_model.bin'''
a : Union[str, Any] = '''diffu... | 705 |
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCamelCase ( _A , _A , _A ) -> List[Any]:
"""simple ... | 19 | 0 |
def _UpperCamelCase ( _A , _A , _A ) -> int:
"""simple docstring"""
def update_area_of_max_square(_A , _A ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
_UpperCAmelCase = update_area_of_max_squar... | 706 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
a : str = '''examples/'''
a : List[str] = {
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(r'''^__... | 19 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transfo... | 707 |
"""simple docstring"""
from __future__ import annotations
def _UpperCamelCase ( _A ) -> None:
"""simple docstring"""
create_state_space_tree(_A , [] , 0 , [0 for i in range(len(_A ) )] )
def _UpperCamelCase ( _A , _A , ... | 19 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .... | 708 |
"""simple docstring"""
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test... | 19 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailabl... | 709 |
"""simple docstring"""
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_t... | 19 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class a_ ( _UpperCAmelCase ):
a ... | 710 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_de... | 19 | 0 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
a : str = logging.get_logger... | 711 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
a : str = True
except (ImportError, ModuleNotFoundError):
a : List[str] = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def _UpperCamelCase ( ... | 19 | 0 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patc... | 712 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_... | 19 | 0 |
"""simple docstring"""
def _UpperCamelCase ( _A ) -> int:
"""simple docstring"""
_UpperCAmelCase = [1]
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase = 0, 0, 0
_UpperCAmelCase = ugly_nums[ia] * 2
_UpperCAmelCase ... | 713 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ... | 19 | 0 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a : Optional[Any] = logging.get_logger(__name__)
a : int = {'''vocab_file''': '''vocab.json'''}
a : Any = {
'''... | 714 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from dat... | 19 | 0 |
"""simple docstring"""
def _UpperCamelCase ( _A ) -> int:
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_UpperCAmelCase = 1
_UpperCAmelCase = 1
while repunit:
_UpperCAmelCase = (1_0 * repunit + 1) % ... | 715 |
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorForma... | 19 | 0 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class a_ :
'''simple docstring'''
pass | 716 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Tuple = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not is_torch_available():
r... | 19 | 0 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def _UpperCamelCase ( _A ) -> Dict:
"""simple docstring"""
if (
(cp >= 0X4E00 and cp <= 0X9FFF)
or (cp ... | 717 |
"""simple docstring"""
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
a : int = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def _UpperCamelCase ( _A ) ... | 19 | 0 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
... | 718 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patc... | 19 | 0 |
"""simple docstring"""
import math
def _UpperCamelCase ( _A ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 ... | 719 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class a_ ( _UpperC... | 19 | 0 |
from typing import Any
class a_ :
def __init__( self : int , __UpperCamelCase : Any ) ->int:
'''simple docstring'''
_UpperCAmelCase = data
_UpperCAmelCase = None
class a_ :
def __init... | 720 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
a : Optional[Any] = '''\
@misc{chen2021evaluating,
title={Eva... | 19 | 0 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class a_ ( _UpperCAmelCase ):
a : ... | 721 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _UpperCamelCase ( _A , _A , _A , _A , _A ) -> np.array:
"""simple docstring"""
_UpperCAmelCase = int(np.ceil((x_end - xa) / step_size ) )
_U... | 19 | 0 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _UpperCamelCase ( ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = ArgumentParser(
d... | 700 |
"""simple docstring"""
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditional... | 19 | 0 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : List[str] = {
'''facebook/encodec_24khz''': '''https://huggingface.co/facebook/e... | 701 |
"""simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def _UpperCamelCase ( _A , _A=None ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = N... | 19 | 0 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline | 702 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class a_ ( _UpperCAmelCase ):
a : Any = ['image_processor', 'tokenizer']
a : Optional[int] = 'AutoImageProcessor'
a : An... | 19 | 0 |
"""simple docstring"""
import math
def _UpperCamelCase ( _A ) -> bool:
"""simple docstring"""
_UpperCAmelCase = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_A )
def _UpperCamelCase ( _A = 1 / 1_2_3_4_5 ... | 703 |
"""simple docstring"""
import colorsys
from PIL import Image # type: ignore
def _UpperCamelCase ( _A , _A , _A ) -> float:
"""simple docstring"""
_UpperCAmelCase = x
_UpperCAmelCase = y
for step in range(_A ): # noqa: B007
... | 19 | 0 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from trans... | 704 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a_ ( nn.Module ):
def __init__( self : List[str] , __UpperCamelCase : int = 16 , __UpperCamelCase : ... | 19 | 0 |
"""simple docstring"""
from __future__ import annotations
def _UpperCamelCase ( _A ) -> bool:
"""simple docstring"""
if len(_A ) < 2:
raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
if any(i <= 0 for i in nums ):
... | 705 |
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCamelCase ( _A , _A , _A ) -> List[Any]:
"""simple ... | 19 | 0 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 706 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
a : str = '''examples/'''
a : List[str] = {
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(r'''^__... | 19 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : Any = {
'''configuration_blenderbot''': [
'''BLENDERBOT... | 707 |
"""simple docstring"""
from __future__ import annotations
def _UpperCamelCase ( _A ) -> None:
"""simple docstring"""
create_state_space_tree(_A , [] , 0 , [0 for i in range(len(_A ) )] )
def _UpperCamelCase ( _A , _A , ... | 19 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
a : Optional[int] = logging.get_logger(__name__)
class a_ ( _UpperCAmelCase ):
def __init__( self : str , *__UpperCamelCase ... | 708 |
"""simple docstring"""
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test... | 19 | 0 |
"""simple docstring"""
def _UpperCamelCase ( ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = [3_1, 2_8, 3_1, 3_0, 3_1, 3_0, 3_1, 3_1, 3_0, 3_1, 3_0, 3_1]
_UpperCAmelCase = 6
_UpperCAmelCase = 1
_UpperCAmelCase = 1_9_0_1
_Uppe... | 709 |
"""simple docstring"""
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_t... | 19 | 0 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _UpperCamelCase ( ) -> str:
"""simple docstring"""
_UpperCAmelCase = HfArgumentParser(_A )
_UpperCAmelCase = parser.parse_args_into_datacla... | 710 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_de... | 19 | 0 |
"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class a_ :
def __init__( self : Any , __UpperCamelCase : Optional[int] ) ->Tu... | 711 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
a : str = True
except (ImportError, ModuleNotFoundError):
a : List[str] = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def _UpperCamelCase ( ... | 19 | 0 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class a_ ( _UpperCAmelCase ):
'''simple docstring'''
a : Any = ['image_processor', 'tokenizer']
a : Optional[int] = 'AutoI... | 712 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_... | 19 | 0 |
"""simple docstring"""
def _UpperCamelCase ( _A ) -> int:
"""simple docstring"""
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_A , _A ):
raise TypeError("""Input value must be a 'int' typ... | 713 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ... | 19 | 0 |
"""simple docstring"""
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
... | 714 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from dat... | 19 | 0 |
"""simple docstring"""
from __future__ import annotations
def _UpperCamelCase ( _A ) -> None:
"""simple docstring"""
create_state_space_tree(_A , [] , 0 , [0 for i in range(len(_A ) )] )
def _UpperCamelCase ( _A , _A , ... | 715 |
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorForma... | 19 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaS... | 716 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Tuple = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not is_torch_available():
r... | 19 | 0 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class a_ ( _UpperCAmelCase ):
a : List[Any] = (DDPMScheduler,)
def _snake_case ( self : List[str] , **__UpperCamelCase : ... | 717 |
"""simple docstring"""
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
a : int = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def _UpperCamelCase ( _A ) ... | 19 | 0 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
a : Any = logging.get_logger(__name__)
class a_ ( _UpperCAmelCase ):
def __init__( self : Optional[int] , *__UpperCamelCase : str , **__UpperCamelCase... | 718 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patc... | 19 | 0 |
"""simple docstring"""
import fire
from utils import calculate_rouge, save_json
def _UpperCamelCase ( _A , _A , _A=None , **_A ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = [x.strip() for x in open(_A ).readlines()]
_Upper... | 719 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class a_ ( _UpperC... | 19 | 0 |
def _UpperCamelCase ( _A , _A , _A , _A ) -> bool:
"""simple docstring"""
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path )
... | 720 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
a : Optional[Any] = '''\
@misc{chen2021evaluating,
title={Eva... | 19 | 0 |
from typing import List
from .keymap import KEYMAP, get_character
def _UpperCamelCase ( _A ) -> Optional[int]:
"""simple docstring"""
def decorator(_A ):
_UpperCAmelCase = getattr(_A , """handle_key""" , [] )
handle += [key]
... | 721 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _UpperCamelCase ( _A , _A , _A , _A , _A ) -> np.array:
"""simple docstring"""
_UpperCAmelCase = int(np.ceil((x_end - xa) / step_size ) )
_U... | 19 | 0 |
"""simple docstring"""
import numpy as np
a : Union[str, Any] = [
['''a''', '''b''', '''c''', '''d''', '''e'''],
['''f''', '''g''', '''h''', '''i''', '''k'''],
['''l''', '''m''', '''n''', '''o''', '''p'''],
['''q''', '''r''', '''s''', '''t''', '''u'''],
['''v''', '''w''', '''x''', ''... | 700 |
"""simple docstring"""
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditional... | 19 | 0 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
a : int = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_copies # noqa: E402
# This i... | 701 |
"""simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def _UpperCamelCase ( _A , _A=None ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = N... | 19 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
a : Optional[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class a_ ( _Up... | 702 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class a_ ( _UpperCAmelCase ):
a : Any = ['image_processor', 'tokenizer']
a : Optional[int] = 'AutoImageProcessor'
a : An... | 19 | 0 |
"""simple docstring"""
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class a_ ( pl.LightningModule ):
def __init__( self : List[str] , __UpperCamelCase : Unio... | 703 |
"""simple docstring"""
import colorsys
from PIL import Image # type: ignore
def _UpperCamelCase ( _A , _A , _A ) -> float:
"""simple docstring"""
_UpperCAmelCase = x
_UpperCAmelCase = y
for step in range(_A ): # noqa: B007
... | 19 | 0 |
"""simple docstring"""
def _UpperCamelCase ( _A ) -> str:
"""simple docstring"""
_UpperCAmelCase = int(_A )
if decimal in (0, 1): # Exit cases for the recursion
return str(_A )
_UpperCAmelCase ,_UpperCAmelCase = divmod(_A , 2 ... | 704 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a_ ( nn.Module ):
def __init__( self : List[str] , __UpperCamelCase : int = 16 , __UpperCamelCase : ... | 19 | 0 |
"""simple docstring"""
import math
import flax.linen as nn
import jax.numpy as jnp
def _UpperCamelCase ( _A , _A , _A = 1 , _A = 1 , _A = 1.0e4 , _A = False , _A = 1.0 , ) -> jnp.ndarray:
"""simple docstring"""
assert times... | 705 |
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCamelCase ( _A , _A , _A ) -> List[Any]:
"""simple ... | 19 | 0 |
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTes... | 706 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
a : str = '''examples/'''
a : List[str] = {
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(r'''^__... | 19 | 0 |
"""simple docstring"""
from random import randint, random
def _UpperCamelCase ( _A , _A , _A , _A = False , _A = False , _A = 5 , ) -> list:
"""simple docstring"""
_UpperCAmelCase = [[-1] * number_of_cells] # Create a highwa... | 707 |
"""simple docstring"""
from __future__ import annotations
def _UpperCamelCase ( _A ) -> None:
"""simple docstring"""
create_state_space_tree(_A , [] , 0 , [0 for i in range(len(_A ) )] )
def _UpperCamelCase ( _A , _A , ... | 19 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import M... | 708 |
"""simple docstring"""
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test... | 19 | 0 |
"""simple docstring"""
import heapq
def _UpperCamelCase ( _A ) -> set[int]:
"""simple docstring"""
_UpperCAmelCase = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Q... | 709 |
"""simple docstring"""
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_t... | 19 | 0 |
"""simple docstring"""
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
a : Optional[int] = None
try:
import msvcrt
except ImportError:
a : Optional[Any] = None
try:
import fcntl
except ImportError:
a : List[str] = None
# Bac... | 710 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_de... | 19 | 0 |
"""simple docstring"""
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class a_ ( _UpperCAmelCase ):
# to overwrite at feature extractactor specific... | 711 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
a : str = True
except (ImportError, ModuleNotFoundError):
a : List[str] = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def _UpperCamelCase ( ... | 19 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : Optional[Any] = {
'''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/mai... | 712 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_... | 19 | 0 |
"""simple docstring"""
import string
from math import logaa
def _UpperCamelCase ( _A , _A ) -> int:
"""simple docstring"""
_UpperCAmelCase = document.translate(
str.maketrans("""""" , """""" , string.punctuation ) ... | 713 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ... | 19 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCamelCase ( _A , _A , _A ) -> List[Any]:
"""simple ... | 714 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from dat... | 19 | 0 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ... | 715 |
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorForma... | 19 | 0 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a : Union[str, Any] = False
class a_ ( unittest.T... | 716 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Tuple = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not is_torch_available():
r... | 19 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Optional[int] = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/co... | 717 |
"""simple docstring"""
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
a : int = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def _UpperCamelCase ( _A ) ... | 19 | 0 |
import math
def _UpperCamelCase ( _A ) -> bool:
"""simple docstring"""
assert isinstance(_A , _A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not n... | 718 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patc... | 19 | 0 |
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def _UpperCamelCase ( ) -> int:
"""simple docstring"""
_UpperCAmelCase ,_UpperCAmelCase = 9, 1_4 # noqa: F841
_UpperCAmelCase = [... | 719 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class a_ ( _UpperC... | 19 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Any = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Opti... | 720 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
a : Optional[Any] = '''\
@misc{chen2021evaluating,
title={Eva... | 19 | 0 |
def _UpperCamelCase ( _A , _A ) -> str:
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(_A , _A ) or not number >= 1:
raise ValueError(
... | 721 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _UpperCamelCase ( _A , _A , _A , _A , _A ) -> np.array:
"""simple docstring"""
_UpperCAmelCase = int(np.ceil((x_end - xa) / step_size ) )
_U... | 19 | 0 |
"""simple docstring"""
def lowerCAmelCase_( SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
assert (
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and number_of_steps > 0
), F'number_of_steps needs to be positive integer, your input {numb... | 20 |
"""simple docstring"""
def lowerCAmelCase_( SCREAMING_SNAKE_CASE = 50_00_00_00 ) -> int:
"""simple docstring"""
UpperCamelCase__ = set()
UpperCamelCase__ = int((limit - 24) ** (1 / 2) )
UpperCamelCase__ = set(range(3 , prime... | 20 | 1 |
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